This version (12 Mar 2022 01:33) was approved by Robin Getz.

ASEE Papers using the Pluto SDR

Incorporating PlutoSDR in the Communication Laboratory and Classroom: Potential or Pitfall?

  • John E. Post P.E. Embry-Riddle Aeronautical University
  • Dennis A. Silage Temple University

The falling price and growing capability of student owned equipment fostering the open laboratory paradigm is revolutionizing the curriculum of many undergraduate analog and digital communication courses in electrical engineering. Among other possibilities, student owned portable equipment facilitates hands-on experiential learning and provides the opportunity to flip the laboratory to increase student engagement. Up until now, this trend has had reduced impact in the area of analog and digital communications because the most capable equipment (such as the Universal Software Radio Peripheral or USRP platform) was too expensive, and inexpensive equipment (such as the ubiquitous RTL SDR dongle) lacked the necessary features for full transceiver implementation. Currently retailing for $99, the Analog Devices ADALM-Pluto (or Active Learning Module PlutoSDR) appears to have the potential to bridge the gap between these two extremes. PlutoSDR is based on the Analog Devices AD9363 RF agile transceiver. This transceiver provides up to 20 MHz of tunable channel bandwidth between 325 MHz to 3.8 GHz, although it is possible to extend the lower frequency range down to 70 MHz in at least one application. It is capable of transmitting or receiving 61.44 MSPS in full duplex using separate receive and transmit channels. PlutoSDR has a compact form-factor, is USB powered, and can be controlled by a variety of software packages such as MATLAB, Simulink, or GNU Radio through the USB port, or by custom Hardware Description Language (HDL) software loaded onto PlutoSDR’s internal Xilinx Zynq System-on-Chip device. Although the PlutoSDR can only legally transmit on Industrial, Scientific, and Medical (ISM) bands, experimenters who hold Amateur Radio licenses are able to exploit a much wider frequency range and applications of the PlutoSDR. Additionally, the PlutoSDR provides easily incorporated spectrum analyzer capabilities for emphasizing spectral properties of analog and digital modulation during lectures. This paper will explore potential opportunities, benefits, and pitfalls to be avoided, of incorporating PlutoSDR in the classroom and open laboratory environments. We begin by reviewing the PlutoSDRs hardware capability and limitations and setup requirements. Next, example communication laboratories and demonstrations using PlutoSDR and MATLAB, Simulink, and GNU Radio will be described. Finally, two semester’s worth of student observations and comments on incorporating PlutoSDR into the student experience from XX XX University at XX, XX are presented.

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Experience of IoT Transceiver with Affordable Software Defined Radio Platform

  • Dr. Liang Hong, Tennessee State University

Due to the rapid growth in many applications, Internet of Things (IoT) will be a prominent source for new hires in the engineering field. However, the growth of IoT is outpacing the current workforce with necessary knowledge and skills, such as IoT transceiver and software defined radio (SDR), the two key and highly demanded techniques for IoT communications. In order to blaze a path to introduce these two advanced techniques to future entry-level communication engineers, a project based learning module using affordable SDR platform was developed with experiential learning pedagogy. The learning materials were developed based on well-defined objectives. Rubrics were also developed to assess the learning outcomes. Through this module, the students will not only gain valuable knowledge of the state-of-the-art IoT wireless communications, interact with the real-world wireless signals over-the-air in real-time, but also improve their creative thinking ability, hands-on and programming skills, and capability to deal with many real-world issues and non-idealities. Assessments show that the learning outcomes were met and the educational module and materials were successful in teaching the advanced techniques with hands-on experience in IoT domain. Additional benefits include increased students’ interests in other communication systems and broadened minority participation in the nation's technology workforce.

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Sample-Based Understanding of Wireless Transceivers and Digital Transmission Via Software-Defined Radio

  • Alexander M. Wyglinski, Worcester Polytechnic Institute

This paper presents an educational paradigm for the teaching of wireless transceiver design and digital transmission techniques from a sample-based perspective using compact form-factor software defined radio (SDR) technology. SDR has been extensively leveraged as an educational resource for the instruction of both undergraduate- and graduate-level digital communication courses for approximately a decade. Given decreasing SDR equipment costs coupled with increasing accessibility to communication system software design tools, SDR technology had been incorporated in numerous electrical and computer engineering curricula around the world. Although most of these SDR-based communication courses view the system from a bit-, frame-, or packet-based perspective and are constrained to laboratory environments, we present an educational framework where the curriculum is sample-based, i.e., the entire communication system is viewed from the analog-to-digital converter (ADC) and the digital-to-analog converter (DAC), and the SDR platforms used are sufficiently compact that students can use them anywhere. The curriculum begins with the fundamentals of wireless communication systems engineering and the handling of complex-valued samples produced by and sent to the ADC and DAC, followed by exposure to several practical aspects of wireless transmission and transceiver implementations such as frequency offset, timing correction, and frame synchronization. Once these basic practical design considerations have been addressed, the course continues with the implementation of various modulation (e.g., ASK, PSK, FSK) and coding (e.g., BCH) schemes, with the objective of successfully transmitting ”hello world” and other messages wirelessly over-the-air within a classroom environment. Finally, several advanced topics such as multipath propagation, equalization, and multicarrier modulation are covered. Throughout the course, the students will be working in groups on a comprehensive course design project that synthesizes many of the concepts taught in class. Although this educational paradigm can use any SDR platform capable of handling complex-valued samples (i.e., inphase samples and quadrature samples), the ADALM-PLUTO SDR platform by Analog Devices was used in this course due to its capabilities and compact form factor.

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Design and Outcome of a Course on Software-defined Radio Within the Computer Science Department

  • Marc Lichtman, University of Maryland College Park

Over the last decade there has been a surge in professional work related to Software-Defined Radio (SDR), and more broadly, Digital Signal Processing (DSP) applied to wireless communications. However, we believe there is an inefficiency when it comes to the current higher education system providing enough graduates with an appropriate background to work in these areas. It may stem from the fact that wireless communications, DSP, and SDR are all topics traditionally taught at the graduate level within Electrical and Computer Engineering (ECE). Thus, the majority of persons with the requisite knowledge and interest will be ECE MS and PhD graduates. While many ECE graduate level students are strong coders, software development skills are not the primary focus of traditional ECE programs, at least when compared to that of a typical Computer Science (CS) curriculum. This results in a small pool of candidates for positions in wireless communications and SDR, made up of MS and PhDs in ECE who happened to focus within the area of wireless communications. Only a fraction of those will have strong coding skills, and an even smaller fraction will have experience or coursework related to software development. This causes a dilemma, since the majority work performed by industry focuses on implementation of DSP in software, rather than raw mathematics or pure derivation. It is not to say that deep mathematical understanding is not required for industry, but software focused positions are more abundant. We also want to stress that it’s not just a problem of background knowledge; CS students are simply unlikely to end up in these types of positions, because it’s an area they have never been introduced to, unless they just happened to have taken an ECE course on the topic, or are part of a multidisciplinary graduate research group.

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university/tools/pluto/asee_papers.txt · Last modified: 12 Mar 2022 01:33 by Robin Getz